update test code for recurrent

Author: zsdonghao

tests/test_layers_recurrent.py

@@ -84,6 +84,29 @@
 if net.count_params() != 7160:
     raise Exception("params dont match")
 
+# n_layer=2
+net = tl.layers.EmbeddingInputlayer(inputs=input_data, vocabulary_size=vocab_size, embedding_size=hidden_size, name='emb2')
+net = tl.layers.BiRNNLayer(
+    net, cell_fn=tf.contrib.rnn.BasicLSTMCell, n_hidden=hidden_size, n_steps=num_steps, n_layer=2, return_last=False, return_seq_2d=False, name='birnn2')
+
+# net.print_layers()
+# net.print_params(False)
+#
+# shape = net.outputs.get_shape().as_list()
+# if shape[1:3] != [num_steps, hidden_size * 2]:
+#     raise Exception("shape dont match")
+#
+# if len(net.all_layers) != 2:
+#     raise Exception("layers dont match")
+#
+# if len(net.all_params) != 5:
+#     raise Exception("params dont match")
+#
+# if net.count_params() != 7160:
+#     raise Exception("params dont match")
+#
+# exit()
+
 ## ConvLSTMLayer TODO
 # image_size = 100
 # batch_size = 10
@@ -141,6 +164,20 @@
 if net.count_params() != 4510:
     raise Exception("params dont match")
 
+# n_layer=3
+nin = tl.layers.EmbeddingInputlayer(inputs=input_seqs, vocabulary_size=vocab_size, embedding_size=embedding_size, name='seq_embedding2')
+rnn = tl.layers.DynamicRNNLayer(
+    nin,
+    cell_fn=tf.contrib.rnn.BasicLSTMCell,
+    n_hidden=embedding_size,
+    dropout=(keep_prob if is_train else None),
+    sequence_length=tl.layers.retrieve_seq_length_op2(input_seqs),
+    n_layer=3,
+    return_last=False,
+    return_seq_2d=True,
+    name='dynamicrnn2')
+net = tl.layers.DenseLayer(rnn, n_units=vocab_size, name="o2")
+
 ## BiDynamic Synced input and output
 rnn = tl.layers.BiDynamicRNNLayer(
     nin,
@@ -151,7 +188,7 @@
     return_last=False,
     return_seq_2d=True,
     name='bidynamicrnn')
-net = tl.layers.DenseLayer(rnn, n_units=vocab_size, name="o2")
+net = tl.layers.DenseLayer(rnn, n_units=vocab_size, name="o3")
 
 net.print_layers()
 net.print_params(False)
@@ -173,6 +210,18 @@
 if net.count_params() != 8390:
     raise Exception("params dont match")
 
+# n_layer=2
+rnn = tl.layers.BiDynamicRNNLayer(
+    nin,
+    cell_fn=tf.contrib.rnn.BasicLSTMCell,
+    n_hidden=embedding_size,
+    dropout=(keep_prob if is_train else None),
+    sequence_length=tl.layers.retrieve_seq_length_op2(input_seqs),
+    return_last=False,
+    return_seq_2d=True,
+    name='bidynamicrnn2')
+net = tl.layers.DenseLayer(rnn, n_units=vocab_size, name="o3")
+
 ## Seq2Seq
 from tensorlayer.layers import EmbeddingInputlayer, Seq2Seq, retrieve_seq_length_op2, DenseLayer
 batch_size = 32
@@ -198,7 +247,7 @@
         decode_sequence_length=retrieve_seq_length_op2(decode_seqs),
         initial_state_encode=None,
         dropout=None,
-        n_layer=1,
+        n_layer=2,
         return_seq_2d=True,
         name='Seq2seq')
 net = DenseLayer(net, n_units=10000, act=tf.identity, name='oo')